Search Results for author:
Found 4 papers, 2 papers with code
Boosting-based Construction of BDDs for Linear Threshold Functions and Its Application to Verification of Neural Networks
Some previous work proposes to transform neural networks into equivalent Boolean expressions and apply verification techniques for characteristics of interest.
Self-supervised Representation Learning for Evolutionary Neural Architecture Search
To enhance the predictive performance of neural predictors, we devise two self-supervised learning methods from different perspectives to pre-train the architecture embedding part of neural predictors to generate a meaningful representation of neural architectures.
NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search
In this paper, we propose a neural predictor guided evolutionary algorithm to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors.
AFO-TAD: Anchor-free One-Stage Detector for Temporal Action Detection
Many of the state-of-the-art methods predict the boundaries of action instances based on predetermined anchors akin to the two-dimensional object detection detectors.
